Analysis of Cancer Detection Classification using ML DL

Performance Analysis of Cancer Detection and Classification using ML and DL

Implementation Plan:

Step 1: Initially we load the input images from a real time medical image dataset.

Step 2: Next we apply the Preprocessing step following the further process, using discrete wavelet transformation Algorithm.

Step 3: Next we perform the Features extraction step; in this Step we will implement Fuzzy C-means to give the high performance. Feature extraction helps to reduce the amount of redundant data from the data set.

Step 4: Next we perform the Classification step, in this step we use Deep learning convolutional neural network (CNN).

Step 5: Next, we Predict the diseases classed are caused or not using traditional SVM (Support vector Methods) Algorithm.

Step 6: The performance of these work is measured through the following performance metrics,
6.1: Accuracy
6.2: Precision
6.3: Recall
6.4: F-Score
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Software Requirement:

1. Tool: Python 3.11.4
2. Operating System: Windows 10(64-bit)
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Note:-

1) Please provide the required dataset to implement this process.